PROJECT SUMMARY In the United States alone, there are approximately 200,000 cases of acute respiratory distress syndrome (ARDS) per year with a mortality rate of 26% { 40 % that increases with patient age. The treatment for patients with ARDS is mechanical ventilation, aimed at reinflating the airways and alveoli over time. However, mechanical ventilation can perpetuate alveolar injury through cyclic alveolar collapse and overdistenstion. The protective mechanical ventilation strategy guards against ventilator induced lung injury (VILI) and is the only treatment strategy that has been proven to consistently reduce mortality. However, this individualized strategy requires patient-specific setting of the ventilators, carefully monitoring overdistension and atelectasis, particularly in patients with severe, heterogeneous lung disease. Current practice involves inspection of the pressure-volume curves for a sequence of positive end-expiratory pressure (PEEP) levels, however, the extent of overdistension and atelectasis is unknown to the clinician in the process, and it remains a significant challenge. Currently, there is no widely-used monitoring technique to guide the setting of the ventilator. Electrical impedance tomography (EIT) and ultrasound are complementary non-ionizing modalities appropriate for long-term monitoring in the ICU. They are complementary in the sense that ultrasound provides accurate spatial resolution of the epithelium up to the pleura and provides information about the elastic properties of the tissue, while EIT provides regional ventilation and perfusion distributions inside the lung. In this project, we will design, build, and assess the clinical usefulness of an integrated EIT/ultrasound tomography system for ventilator setting guidance and detection of developing adverse pulmonary conditions. The proposed EIT/UST system is different from all previous studies in that the ultrasound data is tomographic, collected concurrently with the EIT data, and uses a low-frequency continuous wave excitation pattern. The reconstruction algorithms are novel in the use of dynamic a priori information from the complementary modality. The clinical usefulness will be assessed from data collected on animal models with induced lung injury. Due to the safety and non-ionizing nature of EIT and ultrasound, the technology has the potential of being readily translated to clinical use, and this study will pave the way for a larger human trial to investigate the effect of its use on clinical outcomes for patients with ARDS. 1